cacomp | R Documentation |

'cacomp' performs correspondence analysis on a matrix or Seurat/SingleCellExperiment object and returns the transformed data.

'cacomp.seurat' performs correspondence analysis on an assay from a Seurat container and stores the standardized coordinates of the columns (= cells) and the principal coordinates of the rows (= genes) as a DimReduc Object in the Seurat container.

'cacomp.SingleCellExperiment' performs correspondence analysis on an assay from a SingleCellExperiment and stores the standardized coordinates of the columns (= cells) and the principal coordinates of the rows (= genes) as a matrix in the SingleCellExperiment container.

cacomp( obj, coords = TRUE, princ_coords = 3, python = FALSE, dims = NULL, top = 5000, inertia = TRUE, rm_zeros = TRUE, residuals = "pearson", cutoff = NULL, clip = TRUE, ... ) ## S4 method for signature 'matrix' cacomp( obj, coords = TRUE, princ_coords = 3, python = FALSE, dims = NULL, top = 5000, inertia = TRUE, rm_zeros = TRUE, residuals = "pearson", cutoff = NULL, clip = TRUE, ... ) ## S4 method for signature 'dgCMatrix' cacomp( obj, coords = TRUE, princ_coords = 3, python = FALSE, dims = NULL, top = 5000, inertia = TRUE, rm_zeros = TRUE, residuals = "pearson", cutoff = NULL, clip = TRUE, ... ) ## S4 method for signature 'Seurat' cacomp( obj, coords = TRUE, princ_coords = 3, python = FALSE, dims = NULL, top = 5000, inertia = TRUE, rm_zeros = TRUE, residuals = "pearson", cutoff = NULL, clip = TRUE, ..., assay = Seurat::DefaultAssay(obj), slot = "counts", return_input = FALSE ) ## S4 method for signature 'SingleCellExperiment' cacomp( obj, coords = TRUE, princ_coords = 3, python = FALSE, dims = NULL, top = 5000, inertia = TRUE, rm_zeros = TRUE, residuals = "pearson", cutoff = NULL, clip = TRUE, ..., assay = "counts", return_input = FALSE )

`obj` |
A numeric matrix or Seurat/SingleCellExperiment object. For sequencing a count matrix, gene expression values with genes in rows and samples/cells in columns. Should contain row and column names. |

`coords` |
Logical. Indicates whether CA standard coordinates should be calculated. |

`princ_coords` |
Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0). |

`python` |
A logical value indicating whether to use singular-value decomposition from the python package torch. This implementation dramatically speeds up computation compared to 'svd()' in R when calculating the full SVD. This parameter only works when dims==NULL or dims==rank(mat), where caculating a full SVD is demanded. |

`dims` |
Integer. Number of CA dimensions to retain. Default NULL (keeps all dimensions). |

`top` |
Integer. Number of most variable rows to retain. Set NULL to keep all. |

`inertia` |
Logical. Whether total, row and column inertias should be calculated and returned. |

`rm_zeros` |
Logical. Whether rows & cols containing only 0s should be removed. Keeping zero only rows/cols might lead to unexpected results. |

`residuals` |
character string. Specifies which kind of residuals should be calculated. Can be "pearson" (default), "freemantukey" or "NB" for negative-binomial. |

`cutoff` |
numeric. Residuals that are larger than cutoff or lower than -cutoff are clipped to cutoff. |

`clip` |
logical. Whether residuals should be clipped if they are higher/lower than a specified cutoff |

`...` |
Other parameters |

`assay` |
Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments. |

`slot` |
character. The slot of the Seurat assay. Default "counts". |

`return_input` |
Logical. If TRUE returns the input (SingleCellExperiment/Seurat object) with the CA results saved in the reducedDim/DimReduc slot "CA". Otherwise returns a "cacomp". Default FALSE. |

The calculation is performed according to the work of Michael Greenacre. Singular value decomposition can be performed either with the base R function 'svd' or preferably by the faster pytorch implementation (python = TRUE). When working with large matrices, CA coordinates and principal coordinates should only be computed when needed to save computational time.

Returns a named list of class "cacomp" with components U, V and D: The results from the SVD. row_masses and col_masses: Row and columns masses. top_rows: How many of the most variable rows were retained for the analysis. tot_inertia, row_inertia and col_inertia: Only if inertia = TRUE. Total, row and column inertia respectively.

If return_imput = TRUE with Seurat container: Returns input obj of class "Seurat" with a new Dimensional Reduction Object named "CA". Standard coordinates of the cells are saved as embeddings, the principal coordinates of the genes as loadings and the singular values (= square root of principal intertias/eigenvalues) are stored as stdev. To recompute a regular "cacomp" object without rerunning cacomp use 'as.cacomp()'.

If return_input =TRUE for SingleCellExperiment input returns a SingleCellExperiment object with a matrix of standardized coordinates of the columns in reducedDim(obj, "CA"). Additionally, the matrix contains the following attributes: "prin_coords_rows": Principal coordinates of the rows. "singval": Singular values. For the explained inertia of each principal axis calculate singval^2. "percInertia": Percent explained inertia of each principal axis. To recompute a regular "cacomp" object from a SingleCellExperiment without rerunning cacomp use 'as.cacomp()'.

Greenacre, M. Correspondence Analysis in Practice, Third Edition, 2017.

# Simulate scRNAseq data. cnts <- data.frame(cell_1 = rpois(10, 5), cell_2 = rpois(10, 10), cell_3 = rpois(10, 20)) rownames(cnts) <- paste0("gene_", 1:10) cnts <- as.matrix(cnts) # Run correspondence analysis. ca <- cacomp(obj = cnts, princ_coords = 3, top = 5) ########### # Seurat # ########### library(Seurat) set.seed(1234) # Simulate counts cnts <- mapply(function(x){rpois(n = 500, lambda = x)}, x = sample(1:20, 50, replace = TRUE)) rownames(cnts) <- paste0("gene_", 1:nrow(cnts)) colnames(cnts) <- paste0("cell_", 1:ncol(cnts)) # Create Seurat object seu <- CreateSeuratObject(counts = cnts) # Run CA and save in dim. reduction slot seu <- cacomp(seu, return_input = TRUE, assay = "RNA", slot = "counts") # Run CA and return cacomp object ca <- cacomp(seu, return_input = FALSE, assay = "RNA", slot = "counts") ######################## # SingleCellExperiment # ######################## library(SingleCellExperiment) set.seed(1234) # Simulate counts cnts <- mapply(function(x){rpois(n = 500, lambda = x)}, x = sample(1:20, 50, replace = TRUE)) rownames(cnts) <- paste0("gene_", 1:nrow(cnts)) colnames(cnts) <- paste0("cell_", 1:ncol(cnts)) logcnts <- log2(cnts + 1) # Create SingleCellExperiment object sce <- SingleCellExperiment(assays=list(counts=cnts, logcounts=logcnts)) # run CA and save in dim. reduction slot. sce <- cacomp(sce, return_input = TRUE, assay = "counts") # on counts sce <- cacomp(sce, return_input = TRUE, assay = "logcounts") # on logcounts # run CA and return cacomp object. ca <- cacomp(sce, return_input = FALSE, assay = "counts")

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